Claude for Financial Services is a collection of agents, skills, commands, plugins, and data connectors for investment banking, equity research, private equity, and wealth-management workflows. Financial professionals use it to draft models, memos, research notes, and reconciliations for review by qualified people. The catalogue contains components from these workflows, including agents, skills, plugins, commands, and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add anthropics/financial-services --skill xlsx-authorgit clone --depth 1 https://github.com/anthropics/financial-servicesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/anthropics/financial-services/xlsx-author)<a href="https://agentmods.dev/skills/anthropics/financial-services/xlsx-author"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/xlsx-author/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/anthropics/financial-services/xlsx-author"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/xlsx-author.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.00461 |
| Opus 5 | $0.00016 | $0.00230 |
| Sonnet 5 | $0.00006 | $0.00092 |
| Haiku 4.5 | $0.00003 | $0.00046 |
Grade A, and why
xlsx-author scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- xlsx-author — 100% identical, 0 lines differ
What it actually says
xlsx-author
Use this skill when running headless (managed-agent / CMA mode) and you need to deliver an Excel workbook as a file artifact rather than editing a live workbook via mcp__office__excel_*.
Output contract
- Write to
./out/<name>.xlsx. Create./out/if it does not exist. - Return the relative path in your final message so the orchestration layer can collect it.
How to build the workbook
Write a short Python script and run it with Bash. Use openpyxl:
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill
wb = Workbook()
ws = wb.active; ws.title = "Inputs"
ws["B2"] = "Revenue"; ws["C2"] = 1_250_000_000
ws["C2"].font = Font(color="0000FF") # blue = hardcoded input
calc = wb.create_sheet("DCF")
calc["C5"] = "=Inputs!C2*(1+Inputs!C3)" # black = formula
wb.save("./out/model.xlsx")
Conventions (mirror audit-xls)
- Blue / black / green. Blue = hardcoded input, black = formula, green = link to another sheet/file.
- No hardcodes in calc cells. Every calculation cell is a formula; every input lives on an Inputs tab.
- Named ranges for any value referenced from a deck or memo.
- Balance checks. Include a Checks tab that ties (BS balances, CF ties to cash, etc.) and surfaces TRUE/FALSE.
- One model per file. Do not append to an existing workbook unless explicitly asked.
When NOT to use
If mcp__office__excel_* tools are available (Cowork plugin mode), use those instead — they drive the user's live workbook with review checkpoints. This skill is the file-producing fallback for headless runs.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 43 lines · 32 tokens per session scan A 85b5b76901a7
xlsx-author is a skill published in the GitHub repository anthropics/financial-services (34,762 stars, last pushed 15d ago), licensed Apache-2.0. It adds 32 tokens to every session and 461 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
dcf-model
Build discounted cash flow valuation workbooks in Excel.
google-drive-sheets
Find, read, export, edit, and manage the user's Google Drive, Docs, Sheets, and Slides through per-user OAuth.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
excel-basic-statistics-and-routing
An Excel workflow for filtering grouped data, calculating averages, extracting row ranges, removing duplicates, and adding totals.
dynamic-percentage-and-large-file-analysis
An Excel analysis workflow that changes its file-processing approach based on file size and calculates selected values and percentages.
large-file-parquet-analysis-and-highlight
A workflow for processing large Excel workbooks by counting their rows, converting them to Parquet when needed, and finding maximum values. Parquet is a data-file format designed for efficient reading.